Health-Focused Route Optimization Using Crowdsourced Activity Data
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Solution Overview
Problem
Current map applications primarily focus on efficient routing, ignoring the opportunity to enhance users' health by promoting physical activity, which is a risk factor for various diseases due to inactivity related to age, work, and lifestyle.
Innovation Solution
A computer-implemented method that uses crowdsourcing to determine exercise routes by analyzing users' health behaviors and location data, developing routes that increase health-related values such as calorie burn, steps, and heart rate, and presenting these routes on a computing device, utilizing a graph traversal algorithm and heatmap to personalize and optimize routes based on user health goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If map applications use traditional efficient routing algorithms to minimize travel time and distance, then routing efficiency is improved, but user health enhancement opportunities are lost
Solution Approach 1:
The routing system changes the optimization parameter from purely efficiency-based (time, distance) to health-based (calorie burn, physical activity). The route recommendation engine calculates multiple routes and selects those that maximize health benefits while still reaching the destination, transforming the routing problem from an efficiency optimization to a health optimization problem.
Solution Approach 2:
The system converts the traditionally harmful effect of sedentary behavior and inactivity into a beneficial outcome by deliberately recommending routes that increase physical activity. What was previously considered inefficiency (longer routes, more time) is reframed as a health benefit, turning the routing system into a tool for disease prevention and health promotion.
2Ease of operation
If routing systems prioritize shortest distance and time to destination, then user convenience is improved, but physical activity levels decrease
Solution Approach 1:
The routing system dynamically adjusts route recommendations based on user health goals, current location, and real-time conditions. Instead of static shortest-path algorithms, the system provides dynamic route suggestions that adapt to user preferences (e.g., calorie burn targets, activity type preferences) and environmental factors, making the health-promoting routes feel personalized and convenient rather than imposed.
Solution Approach 2:
The routing system serves multiple functions simultaneously: it provides navigation to the destination, optimizes for health benefits, and offers personalized route suggestions based on user profiles. The system can switch between different optimization modes (efficiency-focused, health-focused, or balanced), making it universally applicable to different user needs and contexts.
3Adaptability or versatility
If the system collects and analyzes extensive user health data and location information, then route personalization is improved, but data processing complexity increases
Solution Approach 1:
The data processing system segments user information into distinct categories (health metrics, location data, preferences, demographic information) and processes each segment separately. The route recommendation engine uses modular processing where different data segments are analyzed independently and then integrated to generate personalized routes, reducing overall system complexity through structured data organization.
Solution Approach 2:
The system introduces intermediary processing layers that aggregate and preprocess raw user data before feeding it into the route optimization algorithm. Data intermediaries (such as health behavior analyzers and location processors) transform raw data into meaningful features that the routing engine can efficiently utilize, reducing the computational burden on the core routing system.
Data Source
AI summary
A computer-implemented method for determining routes includes receiving a user's location and health measures, for a plurality of users, analyzing the plurality of users and their corresponding locations to determine health behaviors at a given location and time, receiving a request from a user for a route, developing a route to the user's destination from a current location to obtain a target health behavior within a given threshold or constraint based on the plurality of user's healthy behaviors, and presenting the a route on a computing device.